This proposed project will develop and make available to the scientific community the Latin American Biobank of Severe Mental Illness (LAB-SMI). This resource, a databank and biobank (including DNA samples and both phenotypic and genotypic data) for 50,000 individuals affected with SMI together with an equal number of controls matched to the cases by age, gender, and geographical location will constitute a major step in reversing the underrepresentation of Latin American populations in psychiatric genetics research. All of the participants will be members of the ?Paisa?, a genetically and culturally homogenous population that is the predominant ethnic group in a region of Colombia (CO) that is home to ~9M people. Cases will consist of individuals affected with psychotic or mood disorders who will be ascertained, agnostic to diagnosis, through the electronic medical records (EMR) of five large psychiatric hospitals in the ?Paisa region?. Controls will be individuals without a history of an SMI diagnosis who will be ascertained through the databases of SURA, one of the largest providers of primary care services in this region. By extracting a wide range of information from the EMR, using methods developed in an existing investigation of SMI in a single Paisa-region hospital, the project team will record lifetime designations of an SMI diagnosis and the presence or absence of SMI-related symptoms (such as delusions) and behaviors (such as suicide attempt). It is hypothesized that these phenotypic features may share genetic causation that transcends diagnostic categories. SNP genotyping and whole exome sequencing studies of the entire 100K LAB-SMI will be well powered to identify novel associations across the allele frequency spectrum for SMI diagnoses, symptoms, and behaviors, and to help identify the causal variation responsible for previously discovered associations. Additionally, the project will use the LAB-SMI databank to explore the architecture of SMI phenotypes. Specifically, it will investigate the genetic basis of different disease trajectories, and mine the full set of phenotype data for association with loci shown previously (through this project or others) to contribute to SMI diagnoses, symptoms, or behaviors.